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Captia Technology
Captia Consulting

AI Opportunity Diagnosis

We identify where artificial intelligence can generate real value in your operation and design the path: use cases assessed on your real data and processes, with an objective basis for deciding before you invest.

AI opportunity diagnosis in operations

An AI opportunity diagnosis is the work done before any code is written: analysing a real operation to pinpoint where artificial intelligence can deliver measurable value, with which data and in what order. Most industrial AI failures are not technical; they come from picking the wrong problem, or one the available data cannot support. This service screens that out before any money is committed.

The assessment covers three fronts. First, where AI creates value: processes that generate continuous data, involve frequently repeated decisions and carry a quantifiable cost of error, with typical use-case families in equipment prediction, quality, planning and unstructured documentation. Second, data maturity: whether the required variables are captured, how much history exists, its quality, its accessibility across PLCs, spreadsheets and ERPs, and whether operational context is recorded. Third, prioritisation across value, feasibility and risk, usually starting with a viable, clearly valuable case rather than the most ambitious one.

The deliverable is a prioritised path: data preparation, integration with plant systems, success criteria and ownership for each use case. Technical execution then belongs to Captia’s industrial AI unit, and the sequence can be framed as a wider transformation roadmap when needed.

How it connects to the system

This solution fits the Captia architecture: it defines the diagnosis and prioritisation frame that activates Connect, AI, Energy and Service.

Frequently asked questions

What is an AI opportunity diagnosis in operations?
It is an analysis performed before any AI project that answers three questions: where AI can generate real value in the operation, whether the available data supports it, and which use cases should come first. The deliverable is a prioritised list of opportunities with an implementation path, not a technical build.
Do I need historical data before starting an AI project?
Not always, but it shapes the order of work. Some use cases require long histories (failure prediction, for instance) while others run on data the plant already produces daily. The diagnosis assesses the data maturity of each process and, where the base is missing, defines what to capture before investing in models.
How is this different from a turnkey AI project?
The deliverable here is an informed decision: which use cases make sense, with which data, in which order and with what risk. The technical execution of the models belongs to Captia’s AI unit. Separating diagnosis from delivery avoids committing to a technology before knowing whether the problem needs it.
How long does the diagnosis phase take?
It depends on scope: how many processes are assessed, how scattered the data is and how available the plant team is. It is generally a matter of weeks rather than months, because the goal is to prioritise and chart the path, not to build the solution. The exact scope is agreed at the start.